Image processing method, apparatus and computer program

ABSTRACT

The present invention provides a novel algorithm for salience detection based on a dual rail antagonistic structure to predict where people look in images in a free-viewing condition. Furthermore, the proposed algorithm can be effectively applied to both still and moving images in visual media without any parameter tuning in real-time.

The present invention relates to image processing and in particular tomethods, apparatus and computer programs for automatically identifyingregions of interest in an image or scene.

It is known that a human observer of an image or scene does not devoteequal attention to all parts of the visible scene or image but rathercertain features will catch the eye more than others. In various fieldsit is desirable to know what features in an image or scene will attractthe user's attention most. For example, when designing a user interface(e.g. a GUI for a computer system, a control panel for a machine or adashboard for a vehicle) it is important to ensure that the mostimportant information or status indicators come first to the user'sattention. Another example is signage, e.g. in buildings where it isdesirable that emergency exit notices stand out or in transportationwhere signs and signals need to be easily identified and interpretedwithout undue distraction to drivers.

A known approach to identifying the areas in an image or scene that willattract attention is to have test subjects view the image or scenewhilst being monitored by an eye tracking device. The eye trackingdevice observes the eyes of the test subject and works out where he orshe is looking. This approach is time consuming, especially as it isnecessary to use many test subjects to obtain an unbiased result.

According to the invention, there is provided a method of processing animage to identify conspicuous regions thereof, the method comprising:

receiving an input image;

deriving first and second antagonistic images from the input image; and

obtaining a conspicuity map based on the first and second antagonisticimages.

Embodiments of the present invention can therefore provide an automaticand objective determination of which parts of an image or scene willattract the attention of an observer. The use of two antagonistic imagesimproves the accuracy of the results. For the purpose of the presentinvention, antagonistic images are images that encode data from onechannel but with opposite senses. In one of a pair of antagonisticimages a high channel value is encoded as a high signal value whilst inthe other of the pair a high channel value is encoded as a low signalvalue. In the case of a luminance channel, one of the pair ofantagonistic images may be the original image and the other an invertedimage. In the case of color channels, the pair of antagonistic imagesmay be different color difference signals.

The use of antagonistic images can be considered as analogous to thehuman visual system which encodes information from the eyephotoreceptors in the form of ON-center and OFF-center pathwaysprojecting to central visual structures from the retina. These twopathways originate at the bipolar cell level: one class of bipolar cellsbecomes hyperpolarized in response to light, as do all photoreceptorcells, and the other class becomes depolarized on exposure to light,thereby inverting the receptor signal; it is the difference betweenthese pathways that is further processed. This antagonistic encoding canalso be found in color perception where it is the balance between twoseparate channels that is encoded rather than just a single signal. Forexample the differences in, red versus green and blue versus yellow.

In the primary visual cortex, different cells detect features such ascolor, luminance, orientation and motion depending on the selectivity oftheir receptive fields. An embodiment of the invention can employ fivefeature channels which analyze the input image: one luminance channel,two color channels, one for orientation and one motion channel. Inputimages are transformed employing the antagonistic approach into positiveand negative features in each of the five channels, again using the twomeasures, the direct and inverse signals to extract the sensoryconspicuity features of the feature channels individually.

In an embodiment if the invention, the antagonistic feature channels arecombined to generate the final salience map, for example using a dynamicweighting procedure ensuring the contribution of each conspicuity map isnever fixed but is instead dependent on the activity peaks in thesignal.

Since the method of the invention requires a relatively lowcomputational effort, embodiments of the present invention can performthis determination in real time using inexpensive hardware. Because realtime processing is possible, the present invention can be applied toproblems for which prior art approaches to determining salience areunsuited. For example, the present invention could be applied inautonomous vehicles or surveillance systems to assist in identifyingobjects requiring attention. In addition, the present invention can beapplied during compression of images and/or video data to identify themore salient parts of the image which can then be encoded with higherfidelity, e.g. higher resolution or higher bitrate, than other,less-salient parts of the image.

Exemplary embodiments of the present invention will be described belowwith reference to the accompanying drawings, in which:

FIG. 1 is a diagram of an image processing method according to anembodiment of the present invention;

FIG. 2 is a diagram of an image processing apparatus according to anembodiment of the present invention;

FIGS. 3A to 3D show an example input image and the effects of variousprocesses applied to it;

FIGS. 4A and 4B show a luminance conspicuity map generated according tothe present invention and according to a prior method respectively;

FIGS. 5A and 5B show an example image and a color conspicuity mapderived therefrom respectively;

FIGS. 6A and 6B show another example image and a color conspicuity mapderived therefrom;

FIGS. 7A and 7B show an example image and an edge conspicuity mapderived therefrom;

FIGS. 8A to 8F show an example input image, results of variousprocessing steps carried out on it and a final salience map according toan embodiment of the invention;

FIGS. 9A and 9B show the effects of different enhancement processescarried out in a salience map according to an embodiment of the presentinvention; and

FIG. 10 shows various sample images together with visual salienceindicated by eye tracking experiments and salience maps generatedaccording to an embodiment of the present invention.

In the following description, like parts depicted in more than onefigure are denoted by like reference numerals. In various Figuresconspicuity and salience values are indicated on a color scale whereblue indicates a low value and red indicates a high value in theconventional manner.

The present invention aims to predict visual attention for an averageobserver with a free viewing task by filtering input image into a numberof low-level visual “feature channels” at the same spatial domain, forfeatures of some or all of color, intensity, orientation and motion(found in visual cortex). The term “free viewing” refers to situationsin which observers are viewing their world without a specific goal. Thepresent invention is based on consideration of low-level mechanisms aswell as the system-level computational architecture according to whichhuman vision is organized.

A method according to an embodiment of the present invention is depictedat a high level in FIG. 1 and is explained further below. FIG. 2 depictsan apparatus according to an embodiment, for carrying out the method.

An apparatus according to an embodiment of the present inventioncomprises a client device 10 which includes a central processing unit 11connected to a storage device 12, input device such as a keyboard 13 anda display 14. Images to be processed in the invention can be capturedusing a still camera 15 or a video camera 16, retrieved from the storagedevice 12 or obtained from another source, e.g. via the internet 20. Thecentral processing unit 11 may include a graphic processing unit GPU inorder to perform parallel calculations optimally.

The current apparatus can take the form of a portable device such as asmart phone or tablet whereby all of the elements—including an imagecapture device, display and touch panel input—are combined into a singlecompact housing. The outputs of the present invention may be stored inthe storage device 12, displayed to the user, or transmitted to anothercomputer. In an embodiment of the invention, some or all of the steps ofthe method can be carried out on a remote server 21 connected to theclient computer 10 via a network 20, such as the internet.

Embodiments of the present invention aim to provide a determination ofthe salience of an image, for example in the form of a salience map. Thesalience (also called salience) of an item—be it an object, a person, apixel, etc.—is the distinct subjective perceptual quality which makessome items in the observed world stand out from their background andimmediately grab our attention. Embodiments of the present invention mayutilize a numerical value to indicate salience which may be determinedin absolute terms or relatively across one or more images.

In the description below, the term “sensory conspicuity features” orsimply “conspicuity features”, is used to refer to features or parts ofan image which are conspicuous, e.g. by crossing a threshold on arelative or absolute scale of salience.

As shown in FIG. 1, the present embodiment of the invention receives S1an input color image I and performs respective processes S2, S3, S4 toobtain one or more conspicuity maps C based on one or more of luminance,color, and spatial frequency (including edges). In an embodiment, amonochrome image can also be used. The conspicuity maps C are combinedS5 to form a salience map S. The salience map S can be enhanced byperforming various enhancement steps, such as applying a blur S6, orincluding motion information S7. The final salience map is output S8,e.g. by displaying it or storing it in memory.

An algorithm S2 for generating a luminance conspicuity map is describedfirst. In an embodiment of the invention, luminance contrast is theprimary variable on which salience computation is based. It is also thefirst type of information extracted by human visual systems in theretina.

A computational model named Division of Gaussians (DoG) can be used forderiving a luminance conspicuity map in real-time. The DoG model isdescribed further in Katramados, I., Breckon, T. P.: ‘Real-time visualsalience by Division of Gaussians’, in 18th IEEE InternationalConference on Image Processing (ICIP), 2011, which document is herebyincorporated by reference in its entirety. The DoG model comprises threedistinct steps to derive a visual salience map.

In the first step, a luminance image U₁ is derived from the input imageI and used to generate a Gaussian pyramid U comprising n levels,starting with image U₁ as the base with resolution w×h. Higher pyramidlevels are derived via down-sampling using a 5×5 Gaussian filter. Thetop pyramid level has a resolution of (w/2^(n-1))×(h/2^(n-1)). Thisimage is referred to as U_(n).

In the second step, U_(n) is used as the top level of a second Gaussianpyramid D to derive its base D₁. In this case, lower pyramid levels arederived via up-sampling using a 5×5 Gaussian filter.

In the third step, an element-by-element division of U₁ and D₁ isperformed to derive the minimum ratio matrix M of their correspondingvalues as described by:

$\begin{matrix}{{M\left( {i,j} \right)} = {\min\;\left( {\frac{D_{1}\left( {i,j} \right)}{U_{1}\left( {i,j} \right)},\ \frac{U_{1}\left( {i,j} \right)}{D_{1}\left( {i,j} \right)}} \right)}} & (1)\end{matrix}$

The luminance conspicuity map is then given by:

C(i,j)=1−M(i,j)   (2)

However, the present embodiment uses both the input image I and itsnegative I′ which provides lower contrast but with a wider dynamicrange. The present embodiment allows investigation of local features ina dual rail antagonistic structure, where the direct and inverse imagesare used to intrinsically derive a luminance conspicuity map. The methodproposed comprises six steps to derive a visual salience map as detailedbelow.

First, the input image I is blurred S2.1, e.g. using a 7×7 Gaussianfilter, to replicate the low-pass spatial filtering which occurs whenthe eye's optical system forms a retinal image. This step can be omittedif the resolution of the input image is low. Exemplary blurred positiveand negative images are shown in FIG. 3A and 3C respectively.

Secondly, relative luminance, Y_(O), and negative luminance, Y_(N), ofthe RGB values of the blurred image I˜ are calculated S2.2, S2.3 as:

Y _(O)=0.5010×r+0.4911×g+0.0079×b   (3)

Y _(N)=255−Y _(O)   (4)

The weights of R, G and B channels were calculated according to theexperimental display characteristics to fit V (λ), the CIE luminosityfunction of standard observer—objects that will be viewed at adistance—https://www.ecse.rpi.edu/˜schubert/Light-Emitting-Diodes-dot-org/Sample-Chapter.pdf.Other weights may be appropriate in other circumstances.

Thirdly, minimum ratio matrices are derived S2.5, S2.6 using the DoGapproach as explained above for both blurred input image, M_(O), andblurred negative image, M_(N), as depicted in FIGS. 3B and 3Drespectively.

Fourthly, an Aggregated Minimum Ratio Matrix M_(A) is calculated S2.7from M_(O) and M_(N) derived from Step 3 as:

M _(A)=(1−λ)M _(O) +λM _(N)   (5)

where tuning parameter λ is derived by using intrinsic image measuresfrom coefficient of variance,

$\frac{\sigma}{\mu},$

of M_(O) and M_(N) as:

$\begin{matrix}{\lambda = \frac{\frac{\sigma}{\mu}\left( M_{O} \right)}{{\frac{\sigma}{\mu}\left( M_{O} \right)} + {\frac{\sigma}{\mu}\left( M_{N} \right)}}} & (6)\end{matrix}$

Fifthly, a normalised Minimum Ratio Matrix M_(Y) is derived S2.8 fromM_(A) and λ derived from Step 3 and 4 as:

$\begin{matrix}{M_{Y} = \frac{M_{A} - M_{A_{\min}}}{M_{A_{\max}} - M_{A_{\min}}}} & (7)\end{matrix}$

Sixthly, a luminance conspicuity map C_(Y) is derived S2.9 from (5) and(7) as:

C _(Y)(i,j)=1−M _(Y)(i,j)   (8)

The luminance conspicuity map C_(Y) for the example image is shown inFIG. 4A alongside the corresponding map generated by the DoG method,FIG. 4B, for comparison.

Next a method S3 for generating a color conspicuity map is described.

Color opponencies are central to modelling the contribution of color tosalience. To this end, the RGB values of the color input image aremapped S3.1 onto red-green (RG) and blue-yellow (BY) opponency featuresin a way that largely eliminates the influence of brightness. The colorconspicuity map can be computed as follows.

First, dual antagonistic color opponencies are computed as:

$\begin{matrix}{{F_{1} = \frac{r - g}{\max\left( {r,g,b} \right)}},{F_{2} = \frac{g - r}{\max\left( {r,g,b} \right)}}} & (9) \\{{F_{3} = \frac{b - {\min\left( {r,g} \right)}}{\max\left( {r,g,b} \right)}},{F_{4} = \frac{{\min\left( {r,g} \right)} - b}{\max\left( {r,g,b} \right)}}} & (10)\end{matrix}$

when the values of F₁, F₂, F₃ and F₄ are negative, these values are setto zero.

Secondly, RG and BY features are derived S3.2 from dual antagonisticcolor opponencies:

$\begin{matrix}{{{RG} = {{\left( {1 - \alpha} \right)F_{1}} + {\alpha F_{2}}}},{{BY} = {{\left( {1 - \beta} \right)F_{3}} + {\beta F_{4}}}}} & (11) \\{{\alpha = \frac{\frac{\sigma}{\mu}\left( F_{1} \right)}{{\frac{\sigma}{\mu}\left( F_{1} \right)} + {\frac{\sigma}{\mu}\left( F_{2} \right)}}},{\beta = \frac{\frac{\sigma}{\mu}\left( F_{3} \right)}{{\frac{\sigma}{\mu}\left( F_{3} \right)} + {\frac{\sigma}{\mu}\left( F_{4} \right)}}}} & (12)\end{matrix}$

where tuning parameters α and β are derived by using intrinsic imagemeasures from coefficient of variance,

$\frac{\sigma}{\mu},$

of dual antagonistic color opponencies. When the intensity value of apixel in a scene image is very small, the color information of the pixelis hardly perceived. Thus, to avoid large fluctuations of the coloropponency values at low luminance, RG and BY are set to zero atlocations with max(r,g,b)<1/10 assuming a dynamic range of [0,1].

Thirdly, the color conspicuity map, C_(C), is derived S3.3 from (11) and(12) as:

C _(C)(i,j)=BY(i,j)+RG(i,j)   (13)

Examples of color conspicuity maps are shown in FIGS. 5A, 5B, 6A, 6B,where A shows the original image and B the resulting color conspicuitymap.

Next an algorithm S4 for generating an edge (orientation) conspicuitymap is described. Biological visual systems are highly adapted to theimage statistics of the natural world. A particularly important aspectof the statistics of natural scenes is the arrangements of edges theycontain. Edges are not arranged randomly, and the structure in theirarrangements is important for shape recognition and texturediscrimination. In an embodiment of the invention, an edge orientationconspicuity map is calculated as set out below:

First, Scharr gradient operators, e.g. of size 3×3, are used tocalculate S4.1 the dominant edge orientation in the image, as below:

$\begin{matrix}{{d_{x} = \begin{bmatrix}{- 3} & {{- 1}0} & {- 3} \\0 & 0 & 0 \\3 & {10} & 3\end{bmatrix}},{d_{y} = \begin{bmatrix}{- 3} & 0 & 3 \\{{- 1}0} & 0 & {10} \\{- 3} & 0 & 3\end{bmatrix}}} & (14) \\{{d_{xy} = \begin{bmatrix}{{- 1}0} & {- 3} & {- 0} \\{- 3} & 0 & 3 \\0 & {10} & 3\end{bmatrix}},{d_{yx} = \begin{bmatrix}0 & {- 3} & {{- 1}0} \\3 & 0 & {- 3} \\{10} & 3 & 0\end{bmatrix}}} & (15)\end{matrix}$

Secondly, D₁, D₂ and D_(A) features are computed S4.2, S4.3 byconvolving the intensity image, Y_(o), with dual antagonistic edgeorientation kernels.

$\begin{matrix}{{D_{1} = {{\left( {1 - \alpha} \right)Y_{O}*d_{x}} + {\alpha\; Y_{O}*d_{y}}}},\mspace{31mu}{D_{2} = {{\left( {1 - \beta} \right)Y_{O}*d_{xy}} + {\beta\; Y_{O}*d_{yx}}}}} & (16) \\{\alpha = {{\left( \frac{\frac{\sigma}{\mu}\left( {Y_{O}*d_{x}} \right)}{{\frac{\sigma}{\mu}\left( {Y_{O}*d_{x}} \right)} + {\frac{\sigma}{\mu}\left( {Y_{O}*d_{y}} \right)}} \right)^{2}\mspace{31mu}\beta} = \left( \frac{\frac{\sigma}{\mu}\left( {Y_{O}*d_{xy}} \right)}{\frac{\sigma}{\mu}\left( {{Y_{O}*d_{{xy})}} + {\frac{\sigma}{\mu}\left( {Y_{O}*d_{yx}} \right)}} \right.} \right)^{2}}} & (17) \\{\mspace{79mu}{D_{A} = {{\left( {1 - y} \right)D_{1}} + {yD_{2}}}}} & (18) \\{\mspace{79mu}{\gamma = \left( \frac{\frac{\sigma}{\mu}\left( D_{1} \right)}{{\frac{\sigma}{\mu}\left( D_{1} \right)} + {\frac{\sigma}{\mu}\left( D_{2} \right)}} \right)^{2}}} & (19)\end{matrix}$

where tuning parameters α, β and γ are derived by using intrinsic imagemeasures from coefficient of variance,

$\frac{\sigma}{\mu},$

of dual antagonistic edge orientations.

Thirdly, the edge orientation conspicuity map, C_(E) is derived S4.4 bynormalizing D_(A).

$\begin{matrix}{C_{E} = \frac{D_{A} - D_{A_{\min}}}{D_{A_{\max}} - D_{A_{\min}}}} & (20)\end{matrix}$

FIGS. 7A and 7B show an example input image and the resulting edgeconspicuity map.

The salience map is then derived by combining one or more of theconspicuity maps. One difficulty in combining color, intensity and edgeorientation conspicuity maps into a single scalar salience map is thatthese features represent a priori not comparable modalities, withdifferent dynamic ranges and extraction mechanisms. An embodiment of thepresent invention therefore uses a dynamic weighting procedure by whichthe contribution of each conspicuity map is not fixed but is insteaddependent on the activity peaks of conspicuity levels. A method ofcalculating a salience map from conspicuity maps is described below.

First, statistical data is computed from the selected conspicuity maps

$\begin{matrix}{{\varnothing_{Y} = {\frac{\sigma}{\mu}\left( C_{Y} \right)}},{\varnothing_{C} = {\frac{\sigma}{\mu}\left( C_{C} \right)}},\mspace{14mu}{\varnothing_{E} = {\frac{\sigma}{\mu}\left( C_{E} \right)}},} & (21) \\{{{\hat{\varnothing}}_{Y} = {\mu\left( C_{Y} \right)}},{{\hat{\varnothing}}_{C} = {\mu\left( C_{C} \right)}},\mspace{14mu}{{\hat{\varnothing}}_{E} = {\mu\left( C_{E} \right)}},} & (22) \\{{\sum{= {\varnothing_{Y} + \varnothing_{C} + \varnothing_{E}}}},{\hat{\sum}{= {2 \times \left( {{\hat{\varnothing}}_{Y} + {\hat{\varnothing}}_{C} + {\hat{\varnothing}}_{E}} \right)}}}} & (23)\end{matrix}$

Secondly, a salience map is calculated by dynamically weightingconspicuity maps

S=αC _(C) +βC _(E) +γC _(Y)   (24)

where

$\begin{matrix}{{\alpha = \frac{{\sum\left( {{\hat{\varnothing}}_{C} + {\hat{\varnothing}}_{E}} \right)} + {\hat{\sum}\varnothing_{C}}}{2{\sum\hat{\sum}}}},{\hat{\beta} = {\max\left( {\frac{{\hat{\varnothing}}_{C} + {\hat{\varnothing}}_{Y}}{\sum},\frac{\varnothing_{E}}{\sum{- \varnothing_{E}}}} \right)}},{\hat{y} = {\max\left( {\frac{{\hat{\varnothing}}_{C} + {\hat{\varnothing}}_{E}}{\hat{\sum}},\frac{\varnothing_{Y}}{\sum{- \varnothing_{C}}}} \right)}}} & (25) \\{\mspace{79mu}{{\alpha = \frac{\hat{\alpha}}{\hat{\alpha} + \hat{\beta} + \hat{\gamma}}},{\beta = \frac{\hat{\beta}}{\hat{\alpha} + \hat{\beta} + \hat{\gamma}}},{y = \frac{\hat{\gamma}}{\hat{\alpha} + \hat{\beta} + \hat{\gamma}}}}} & (26)\end{matrix}$

FIGS. 8A to F depict an example input image, the various conspicuitymaps and a resulting salience map.

Various optional enhancements to the salience map calculated asdescribed above can be made. For example, the salience maps can beblurred S5 and/or a bias towards the centre added to emulate fovealvision. In an embodiment a Gaussian Filter, G, (e.g. of size 15×15) isapplied. In an embodiment a central bias map, Sc, e.g. with a Gaussiankernel of 7 with a weight 0.3 is also applied. FIGS. 9A and 9B show,using the example image from FIGS. 3 and 4, the effect of Gaussianfilter G and the combination of the Gaussian filter G and central biasmap Sc respectively. The combined output is calculated as:

Ŝ=0.7×G*S+0.3×S _(C)   (27)

In an embodiment of the invention, ultra-wide angle images, such as 360°images are processed. Such images can be obtained by stitching togetherimages obtained using two or more wide angle imaging systems. Whenprocessing such images to determine salience no central bias is appliedso that a field of view for closer examination can be selected after theevent.

Another optional enhancement is to incorporate motion features. Temporalaspects of visual attention are relevant in dynamic and interactivesetups such as movies and games or where an observer is moving relativeto the observed scene. An embodiment of the invention uses the motionchannel to capture human fixations drawn to moving stimuli (in theprimate brain motion is derived by the neurons at MT and MST regionswhich are selective to direction of motion) by incorporating motionfeatures between pairs of consecutive images in a dynamic stimuli toderive temporal salience, S_(T), as follows:

Firstly, a difference image, DF, is computed from the current, Y_(O)[n],and previous, Y_(O)[n−1], images.

DF=|Y _(O)[n]−Y _(O)[n−1]|  (28)

Secondly, the difference image, DF, is blurred to remove detail andnoise with a Gaussian Filter, G (e.g. of size 15×15)

=G*DF   (29)

Thirdly, motion salience, S_(M), is calculated by applying a hardthreshold to the blurred difference image calculated in Step 2.

$\begin{matrix}{{S_{M}\left( {i,j} \right)} = \left\{ \begin{matrix}{\hat{DF}\left( {i,j} \right)} & {{{if}\mspace{14mu}\left( {i,j} \right)} > 20} \\0 & {else}\end{matrix} \right.} & (30)\end{matrix}$

Fourthly, the motion salience, S_(M), is added to spatial salience, Ŝ,calculated in (27):

S _(T)=0.3×G*S _(M)+0.7×Ŝ  (31)

A performance analysis was performed on an MIT benchmark data set [TilkeJudd, Frodo Durand, and Antonio Torralba,: ‘A Benchmark of ComputationalModels of Saliency to Predict Human Fixations’, in Computer Science andArtificial Intelligence Laboratory Technical Report]. The presentinvention was found to provide a useful approximation of the visualsalience reflected in the eye-tracking data. Results are shown in FIG.10.

Because the computational effort required for the present invention isreasonable, it can be implemented on readily obtainable hardware andstill provide a real-time salience map at a reasonable frame rate.Accordingly, an embodiment of the present invention provides a computerprogram that calculates in real-time a salience map for a screen displayand overlays the salience map on the display in semi-transparent form.The screen display can be the output of another application, the GUI ofan operating system, a pre-recorded moving image, or a feed from animaging device. The overlay can be used for testing of user-interfacesof applications or operating systems, or for review of computer gamesand movies. The salience map generating program can be used in aportable computing device that includes an imaging device, e.g. asmartphone or tablet, to enable a live site survey of a building orother location. The present invention can also be applied inapplications such as control of autonomous vehicles for identifyingobjects requiring attention.

The invention can also be applied to the compression of images,including video signals. In such an embodiment, the salience ofdifferent areas of the image, or of frames of the video signal, isdetermined and used to control the compression process. For example,regions of higher salience are compressed less or encoded with greaterfidelity than regions of lower salience. The regions of higher saliencemay be encoded at a higher resolution, at a higher bitrate and/or at ahigher frame rate or otherwise prioritized over areas with a lowersalience. Different block sizes may be used for regions of highersalience. In this way the image or video signal can be encoded in agiven size or bandwidth whilst achieving a subjectively better output.

Thus the present invention provides a novel algorithm for saliencedetection based on a dual rail antagonistic structure to predict wherepeople look in images in a free-viewing condition. Furthermore, theproposed algorithm can be effectively applied to both still and movingimages in visual media without any parameter tuning in real-time. Anembodiment of the present invention comprises a computer program forcarrying out the above described method. Such a computer program can beprovided in the form of a standard alone application, an update oradd-in to an existing application or an operating system function.Methods of the present invention can be embodied in a functional librarywhich can be called by other applications.

It will be appreciated that the above description of exemplaryembodiments is not limiting and that modifications and variations to thedescribed embodiments can be made.

For example, computational tasks may be performed by more than onecomputing device serially or concurrently. The invention can beimplemented wholly in a client computer, on a server computer or acombination of client- and server-side processing. Certain steps ofmethods of the present invention involve parallel computations that areapt to be implemented on processers capable of parallel computation, forexample GPUs. The present invention is not to be limited save by theappended claims.

1. A method of processing an image to identify conspicuous regionsthereof, the method comprising: receiving an input image; deriving firstand second antagonistic images from the input image; and obtaining aconspicuity map based on the first and second antagonistic images.
 2. Amethod according to claim 1 wherein the first antagonistic image is aluminance image and the second antagonistic image is a negativeluminance image.
 3. A method according to claim 2 wherein obtaining aconspicuity map comprises deriving aggregated minimum ratio matricesusing a Division of Gaussians method.
 4. A method according to claim 3wherein obtaining a conspicuity map comprises performing a weighted sumof the minimum ratio matrices.
 5. A method according to claim 1 whereinthe first and second antagonistic images are RG and BY color opponencyimages.
 6. A method according to claim 1 wherein deriving first andsecond antagonistic images comprises blurring the input image.
 7. Amethod according to claim 1 wherein a plurality of conspicuity maps arederived and further comprising obtaining a salience map from theconspicuity maps.
 8. A method according to claim 7 wherein theconspicuity maps include at least one of: luminance conspicuity maps,color conspicuity maps and edge conspicuity maps.
 9. A method accordingto claim 7 wherein obtaining a salience maps comprises calculating aweighted average of the conspicuity maps using weights dependent on peakvalues of the respective conspicuity maps.
 10. A method according toclaim 7, further comprising blurring and/or center-weighting thesalience map.
 11. A method according to claim 7, wherein the input imageis one of a sequence of images and further comprising calculating amotion salience map based on the input image and one or more precedingimages in the sequence of images; and combining the salience map withthe motion salience map.
 12. A method according to claim 1 furthercomprising displaying the conspicuity map or the salience mapsuperimposed on the input image.
 13. A method according to claim 12further comprising capturing the input image using a camera anddisplaying the conspicuity map or the salience map superimposed on theinput image substantially in real time.
 14. A method of compressing animage or a sequence of images, the method comprising identifyingconspicuous regions of the image or sequence of images using the methodof claim 1 and compressing conspicuous regions with greater fidelitythan other regions of the image.
 15. A computer program comprisingcomputer interpretable code that, when executed on a computer system,instructs the computer system to perform a method according to claim 1.16. An image processing apparatus comprising a processor and a memory,the memory storing a computer program according to claim
 15. 17. Animage processing apparatus according to claim 16 further comprising animage capture device configured to capture an image as the input image.